Procedural Pain Management for Neonates Using Nonpharmacological Strategies
Bibliographic record
Abstract
This is the second of a 2-part series to provide an overview of our current level of knowledge related to nonpharmacological strategies to diminish the pain associated with commonly performed procedures in the NICU. In our first article we discussed the prevalence of repeated pain exposure in the NICU and the importance of nonpharmacological strategies specifically containment or facilitated tucking, swaddling, positioning, nonnutritive sucking, and sweet solutions. These strategies are generally nurse-driven and we believe their importance has been underutilized. In this article we will emphasize the importance of maternal presence as a mediator for pain relief. The efficacy of breastfeeding, maternal skin-to-skin care (often referred to as kangaroo care), and multisensorial stimulation such as auditory and olfactory recognition will be the primary focus of our discussion. In addition, although primarily mother-driven, these strategies are ultimately nurse-enabled, thus the importance of this connection cannot be under appreciated with respect to successful implementation in the NICU.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Clinical review of nonpharmacological strategies for neonatal procedural pain; the object is pain management.
The review concerns pain-management strategies for neonates, not evidence-synthesis methodology.
Clinical review of nonpharmacological neonatal procedural pain management.
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".